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Data Descriptor

Daily and Accumulated Training-to-Match Load Ratios in Professional Soccer: The Influence of Starting Status and Playing Position Across a Full Competitive Season

by
Alejandro Sierra-Casas
1,
Daniel Castillo
2,
Filipe Manuel Clemente
3,4,5 and
Alejandro Rodríguez-Fernández
1,6,*
1
Faculty of Physical Activity and Sports Sciences, Universidad de León, 24071 León, Spain
2
Valoración del Rendimiento Deportivo, Actividad Física y Salud y Lesiones Deportivas (REDAFLED), Faculty of Education, University of Valladolid, 42004 Soria, Spain
3
Applied Research Institute (i2A), Polytechnic University of Coimbra, 3045-601 Coimbra, Portugal
4
Sport Physical Activity and Health Research & Innovation Center, 3045-601 Coimbra, Portugal
5
Department of Biomechanics, Gdansk University of Physical Education and Sport, 80-336 Gdańsk, Poland
6
VALFIS Research Group, Institute of Biomedicine (IBIOMED), Universidad de León, 24071 León, Spain
*
Author to whom correspondence should be addressed.
Data 2026, 11(4), 84; https://doi.org/10.3390/data11040084
Submission received: 11 March 2026 / Revised: 30 March 2026 / Accepted: 6 April 2026 / Published: 14 April 2026
(This article belongs to the Special Issue Big Data and Data-Driven Research in Sports)
Browse Figure
Versions Notes

Abstract

Introduction: Monitoring training load is essential in elite soccer to optimize performance and reduce injury risk. The training-to-match load ratio (TMr) has emerged as a useful metric to contextualize training demands relative to competitive match exposure. The objective of this study was to compare daily and accumulated TMr between starters and non-starters over a professional season, considering microcycle day and playing position. Methods: Twenty players (Tier 3) from a professional team were monitored during a full competitive season (30 microcycles; 144 training sessions; 30 matches). External load variables, namely total distance (TD), high-speed distance (HSD), sprint distance (SPD), high metabolic load distance (HMLD), acceleration (ACC) and deceleration (DCC), were collected using 10 Hz GPS devices (STATSports). Daily and microcycle TMr were calculated relative to each player’s maximal match value registered during a full competitive period. Linear mixed-effects models examined the effects of starting status, microcycle day, and playing position. Results: Linear mixed models revealed significant three-way interactions (status × day × position) for locomotor variables: TD (F = 3.36, p < 0.001), HSD (F = 2.49, p < 0.001), and SPD (F = 3.37, p < 0.001). Starters accumulated higher loads on match day, whereas non-starters showed higher TMr on MD + 1 and MD + 2. Position-specific differences emerged during acquisition sessions (i.e., MD − 5 to MD − 3), particularly for wide midfielders (WMs) and central defenders (CDs). No significant three-way interactions were observed for ACC, DCC, or HMLD absolute loads (p > 0.05), nor for any accumulated microcycle TMr metrics (p > 0.05). Conclusions: TMr effectively differentiates preparation strategies between starters and non-starters. Although “top-up conditioning” sessions increase early-week relative loads for non-starters, position-specific variations–particularly in mechanical variables during acquisition sessions–highlight the need for individualized load prescription.
Dataset: https://doi.org/10.5281/zenodo.18986229; DOI 10.5281/zenodo.18986228.
Dataset License: the Creative Commons Attribution 4.0 International License (CC BY 4.0)

1. Summary

This data descriptor presents a longitudinal, anonymized dataset of external training and match loads from soccer players over a full competitive season. The dataset captures daily and microcycle-level observations categorized by playing status (starters vs. non-starters) and playing position (central defenders, fullbacks, central midfielders, wide midfielders, and forwards). It includes standardized locomotor and mechanical metrics derived from 10 Hz GPS and 100 Hz accelerometry, specifically total distance, high-speed running distance (>19.8 km·h−1), sprinting distance (>25.2 km·h−1), high metabolic load distance (>25.5 W·kg−1), and high-intensity accelerations and decelerations (>3 m·s−2). A unique feature of this dataset is the inclusion of the training-to-match load ratio (TMr), normalized against each player’s maximal individual match demand. These data are intended to support research into elite sports periodization, compensatory training strategies for non-starters (“top-up conditioning”), and position-specific physical demands in professional soccer.

2. Data Description

The dataset is organized into two files (CSV and Excel) to ensure interoperability and transparency. The TMr_season_observations.csv file contains 2375 individual player–session rows in a wide format. Each observation includes the player’s unique ID (anonymized), playing position, starting status, and the specific day within the microcycle (e.g., MD − 4, MD + 1). The metrics provided cover both absolute values (e.g., meters, counts) and relative TMr values (percentages of maximal match demand). The TMr_data_dictionary.csv file defines each variable, including the specific velocity and acceleration thresholds used, units of measurement, and descriptive statistics. Variables are grouped into three domains: Volume (Total Distance), Intensity (HSD, SPD, HMLD), and Mechanical Load (ACC, DCC). The distribution of observations across the microcycle is documented to account for the systematic implementation of recovery and acquisition phases, with a total of 144 training sessions and 30 official matches recorded.

3. User Notes

The “maximal match demand” serves as the individual benchmark for TMr calculations; consequently, daily ratios typically remain below 1.0 (100%). When evaluating non-starters, users should prioritize the analysis of MD + 1 and MD + 2, as these sessions reflect structured compensatory “top-up” work. Please note that goalkeepers are excluded from the dataset. Secondary analyses should account for tactical context and playing style, as these factors directly influence the match demand denominator. Observations for players sidelined by injury or return-to-play protocols are marked as “NA” and were not included in the analysis.

4. Introduction

Training load monitoring has emerged as a critical component in the realm of sports science, particularly for fitness coaches aiming to enhance athletic performance while simultaneously reducing the risk of injuries [1,2]. Training load is conventionally categorized as either internal—referring to the physiological and psychological response to exercise—or external, which quantifies the locomotor and mechanical work performed [3]. In practical applications, the measurement of internal load often presents challenges, leading to a predominant focus on external load analysis [4]. This shift is largely due to the objective nature of external load metrics, which have become increasingly accessible through advancements in tracking technologies [5,6]. In soccer, derived external-load monitoring is widely used to quantify locomotor demands and inform individualized training prescription, although these data are most informative when interpreted alongside internal-load measures [7].
In elite soccer, training is typically periodized around a weekly microcycle, defined as the number of days between two competitive matches whose length may vary depending on the competition schedule [8]. This structure generally begins with recovery or “top-up conditioning” sessions one or two days after match day (i.e., MD + 1 or MD + 2) followed by high-load acquisition days targeting strength, endurance, and speed in the middle of the week (i.e., MD − 5 to MD − 3), and concludes with tapering sessions (i.e., MD − 2 and MD − 1) to ensure freshness for the upcoming match [8,9,10,11]. Recent research highlights that these microcycles must account for contextual factors such as playing position and starting status, as the individual load accumulation varies significantly across the squad [12,13,14].
For starters, match play typically provides the greatest weekly high-intensity locomotor stimulus, whereas non-starters often require compensatory sessions to reduce deficits in high-speed and sprint exposure [15], suggesting that participation in match play itself could be the most appropriate and specific stimulus for preparing players for the physical demands of match play [16]. Consequently, non-starters frequently experience a significant deficit in high-intensity exposure (e.g., high-speed running or sprints), leading to potential decreases in physical fitness over a season [17]. To mitigate this, “top-up conditioning” sessions have become standard practice, designed to replicate match-specific physical demands for players with limited competitive minutes, typically scheduled on MD + 1 or MD + 2 to align with recovery and preparation cycles [18].
To quantify the efficacy of these training interventions, researchers utilize the training-to-match load ratio (TMr), defined as the ratio of training load to match load for specific metrics (e.g., RPE, high-speed running, sprinting) [14]. This metric compares individual training session loads to the demands of match play, providing a normalized view of preparation [11,19]. In practice, as this ratio is calculated to periodize the training load during microcycles, player status is an important factor to consider. Because the acute/chronic workload ratio is conceptually distinct from the training-to-match ratio and remains methodologically debated, it should not be used here as direct justification for a specific TMr target range [20]. For instance, previous studies [16] found that starters covered greater high-speed and sprint distances than non-starters across a full season, not because of differences in training load, but due to the high-intensity nature of match participation. Recent work has shown that non-starters can present higher training-to-match ratios than starters across several external-load measures, consistent with deliberate compensatory-load strategies and the need for individualized prescription according to playing status [14,21].
Despite the growing body of literature on microcycle load accumulation, there is a lack of longitudinal investigation into how the TMr varies daily across a full season when accounting for both playing position and starting status. Therefore, the primary objective of this study was the provision of a comprehensive data descriptor and the comparison of training-to-match load ratio between starters and non-starters over a complete professional season. Furthermore, this investigation established specific benchmarks according to the day of the week and playing positions, offering a practical framework for microcycle periodization based on the validated dataset.

5. Materials and Methods

5.1. Sample

Twenty outfield soccer players (21.0 ± 1.5 years; 174.2 ± 10.8 cm; 72.2 ± 7.7 kg) classified as Tier 3 (Highly Trained/National Level) [22] according to the participant classification framework took part in this study. Goalkeepers were excluded due to the unique physiological demands of their role. The evaluated team finished among the top four at the end of season in their league. Players were classified according to playing positions as central defenders (CDs; n = 4), fullbacks (FBs; n = 3), central midfielders (CMs; n = 5), wide midfielders (WMs; n = 5), and forwards (FWs; n = 3). Playing status was defined based on competitive minutes: starters (initiation of the match and >60 min played) and non-starters (<35 min played) [11,23]. In all cases examined, the starting player initiated the match from the outset. Throughout the season, players engaged in 4–5 training sessions per week (alongside 2 weekly gym sessions), in addition to the official competition match. All participants were familiarized with the monitoring technology during a six-week preseason.

5.2. Design and Ethics Committee

A longitudinal observational study was conducted during a full competitive season, spanning from September to May (a nine-month period). The data collection period spanned 30 competitive microcycles, encompassing 144 training sessions and 30 official matches. The study analyzed the full competitive period, excluding two competitive microcycles with two matches in a week due to its different training load structure. A total of 2375 individual player observations were recorded, categorized by playing status and session type (Table 1).
The microcycle followed a standardized structure based on match-day (MD) proximity [8,11]. The cycle typically commenced with MD + 1 or MD + 2 recovery/“top-up conditioning” sessions. During these sessions, starters engaged in active recovery (mobility and low-intensity aerobic work), while non-starters underwent structured compensatory session designed to replicate match-intensity demands through small-sided games (SSGs) and high-intensity interval running [24]. The acquisition phase occurred between MD − 5 and MD − 3, focusing on tactical–physical integration (strength, endurance, and speed), followed by a progressive tapering phase on MD − 2 and MD − 1 to optimize match-day readiness.
In accordance with ethical standards for professional environments where data collection is a contractual requirement for performance monitoring, individual written informed consent was not required [25,26]. Nevertheless, the study protocol was formally approved by the local ethics committee (ETICA-ULE-004-2021). All procedures adhered to the Declaration of Helsinki [27], and all players were informed of the research purposes and the confidential treatment of their data.

5.3. Methodology

5.3.1. External Training Load

External training load data were quantified using 10 Hz GPS units integrated with 100 Hz tri-axial accelerometers (STATSports, Newry, UK). These devices have demonstrated high validity and inter-unit reliability (error < 5%) for distance and peak speed metrics in professional soccer [28,29]. Devices were activated 15 min before each session to ensure adequate satellite reception and positioned between the scapulae using custom vests. Each player consistently used the same device to minimize inter-unit variability [30]. The following metrics were analyzed to determine external training load: total distance (TD), distance covered at >19.8 km·h−1 (HSD), distance covered at >25.2 km·h−1 (SPD), distance covered at >25.5 W·kg−1 (HMLD), number of accelerations exceeding 3 m·s−2 (ACC), and number of decelerations exceeding −3 m·s−2 (DCC). Thresholds were aligned with recent professional standards and validated manufacturer algorithms [11,31]. Data were processed using the manufacturer’s software (Viper Statsports v4.3.8) and exported to Microsoft Excel.

5.3.2. Training-to-Match Load Ratio

Microcycle external load represents the cumulative of each variable during all training sessions and match. To unify the calculation of match demands and ensure comparability between starters and non-starters, weekly and daily ratios were normalized by dividing the external load values by the individual maximum match demand reordered for each variable during the entire competitive period. Inclusion criteria required players to complete the full duration of a training session or match. Data from injured players or those in return-to-play protocols were excluded from the final analysis. Warm-ups were included in training session analysis but excluded from match data to isolate the real demands of competitive play [32].

5.4. Statistical Analysis

Linear mixed-effects models were employed to analyze the variations in external training load and training-to-match load ratios across the season. Separate models were performed for daily variables and microcycle-level accumulated variables. For daily analyses, fixed effects included playing position, day within the microcycle, and starting status, with all interaction terms. For microcycle-level analyses, fixed effects included starting status, playing position, and microcycle, including their interactions. Type III sums of squares were applied. PlayerID was included as a random intercept to account for repeated observations within players. A variance components covariance structure was specified, and parameters were estimated using restricted maximum likelihood. Where significant main effects or interactions were identified, post hoc comparisons were performed using Bonferroni-adjusted estimated marginal means. Precision of estimates is reported using 95% confidence intervals (95% CIs). Statistical significance was set at p ≤ 0.05. All analyses were performed using IBM SPSS Statistics (v.27, Armonk, NY, USA).

6. Results

6.1. Daily External Load Profile

Descriptive data (mean ± standard deviation) for external load metrics across the standard microcycle, stratified by playing position and starting status, are presented in Table 2. Linear mixed models revealed significant three-way interactions (starting status × microcycle day × playing position) for TD (F = 3.36; p = 0.000), HSD (F = 2.49; p = 0.000) and SPD (F = 3.377; p = 0.000), without interaction effects in ACC (F = 0.619; p = 0.941), DCC (F = 0.766; p = 0.805) and HMLD (F = 0.797; p = 0.766). Post hoc comparisons demonstrated significant differences (p < 0.05) between starters and non-starters during MD, MD + 1, and MD + 2 across all playing positions. Specifically, during MD, starters exhibited markedly higher volumes across all locomotor metrics. Conversely, on MD + 1 and MD + 2, non-starters accumulated significantly higher loads. Notably, for the WMs, non-starters performed significantly higher high-intensity work (HSD, SPD, and HMLD) during the MD − 3 acquisition session compared to starters (p < 0.05). Furthermore, non-starter WMs recorded significantly greater ACC during MD − 2 (p < 0.05).

6.2. Daily Training-to-Match Load Ratios

The daily external load ratios, normalized to individual maximal match demands, are detailed in Table 3. Linear mixed models revealed significant three-way interactions (starting status × TMr × playing position) for TD (F = 2.046; p = 0.001), HSD (F = 1.496; p = 0.046) and SPD (F = 2.764; p = 0.000), without interaction effects in ACC (F = 0.000; p = 0.467), DCC (F = 1.047; p = 0.398) and HMLD (F = 1.266; p = 0.159). In alignment with absolute load findings, the TMr revealed significant disparities between starters and non-starters on MD, MD + 1, and MD + 2 (p < 0.05). Analysis of mid-week acquisition sessions (i.e., MD − 4 and MD − 3) identified position-specific differences in training-to-match preparation. For CDs, the TMr for ACC, DCC, and HMLD was significantly higher in non-starters than in starters during the MD − 4 session (p < 0.05). Additionally, during MD − 3 sessions, CM and WM non-starters achieved significantly higher HMLD ratios compared to their starting counterparts (p < 0.05). No significant status-based differences in the TMr were observed during the tapering phase (MD − 2 and MD − 1) for any position (p > 0.05).

6.3. Microcycle Accumulated Training-to-Match Load Ratios

The microcycle training-to-match load ratios, normalized to individual maximal match demands, are detailed in Figure 1. Linear mixed models revealed no significant three-way interactions (starting status × microcycle ratio × playing position) for TD (F = 0.712; p = 0.973), HSD (F = 0.616; p = 0.997), SPD (F = 0.668; p = 0.983), ACC (F = 0.932; p = 0.652), DCC (F = 0.748; p = 0.951) and HMLD (F = 0.663; p = 0.990).

7. Discussion

This study established a comprehensive longitudinal framework to compare daily and accumulated training-to-match load ratios (TMrs) between starters and non-starters across a full professional season. The central finding indicates that while starters accumulate significantly higher microcycle loads due to match exposure, non-starters exhibit a higher TMr during the early microcycle (MD + 1 and MD + 2) across all positions in comparison to starters ones. By providing these granular benchmarks, and although sample size is limited, our position-specific analysis revealed that certain playing positions such as WM and CD exhibit distinct compensatory load profiles during mid-week acquisition sessions (i.e., MD − 4 and MD − 3). These results potentially reflect the utility of the presented dataset in revealing patterns that are often obscured in aggregate team-level analyses, thereby serving as a validated reference for load prescription in professional settings.
The observed disparities in MD, MD + 1, and MD + 2 are consistent with established periodization models in elite soccer [8,11,33]. The significantly higher locomotor output of non-starters during the immediate post-match period that is the recovery or “top-up conditioning” sessions (i.e., MD + 1/MD + 2) reflect the training strategies designed to mitigate the lack of competitive stimulus [24,34]. Although these sessions successfully elevated the TMr for non-starters [22], this supports the “match play as training” hypothesis, where the unique high-intensity nature of competition provides a stimulus that is difficult to replicate through training alone [16,35,36,37]. Compensatory sessions increased early-week relative exposure in non-starters, but this should not be interpreted as evidence that these sessions replicate match demands, particularly for high-speed running and sprinting [22]. Indeed, our results on acquisition days (in which no significant differences were shown between starters and non-starters, except for WM in HSD and SPD on MD − 3) are in line with those from previous studies [16,34,38]. However, these results contrast with those shown by Stevens et al. [1] in which professional Dutch Eredivisie non-starters’ training was less demanding than regular training sessions, especially on MD − 4.
Related to non-starters’ training stimulus, as players’ physical capacity should be guaranteed to be the same for starters and non-starters, it is not clear that these external load differences between both groups could be insufficient for the the substitutes [39]. On the one hand, high-intensity activity (even at the expense of total load performed) appears to be effective for activate aerobic and anaerobic adaptations [40,41], but on the other hand, game performance and injury risk could be compromised by the reduced total load experienced by non-starters [16,42]. Linked to bridge the gap between players’ load, we cannot overlook the fact that the volume and intensity performed by non-starters through “top-up conditioning” training induce some levels of fatigue that could compromise the following acquisition days of week [43]. In this line, our results from MD − 5, MD − 4 and MD − 3 do not reflect significant differences between starters and non-starters.
While no main effects for starting status were observed during the mid-week acquisition phase (MD − 5 to MD − 3), significant interactions emerged when considering playing position. For instance, WM non-starters recorded significantly higher HSD, SPD, and HMLD on MD − 3 compared to starters. Considering the fact that sample size per playing position is limited, we hypothesize that this may reflect a specific player profile bias; substitutes in these roles often prioritize high-velocity running to maintain “explosive” readiness, whereas starters may undergo more tactical, lower-intensity “refresh” work on these days [44,45,46].
Furthermore, the significantly higher TMr for ACC, DCC, and HMLD observed in CD non-starters on MD − 4 is particularly revealing. The greater ACC/DCC/HMLD ratios in non-starting central defenders may indicate more dense drill exposure relative to their chosen match reference, but this interpretation is dependent on the denominator definition and the use of fixed acceleration thresholds [47]. This may be attributed to the nature of CD match play, which is often characterized by lower metabolic and mechanical densities compared to other playing positions; thus, training drills emphasizing high-density mechanical work (e.g., SSGs with reduced area per player) result in a TMr that exceeds 1.0 (100% of match demands) for this specific position [11]. Moreover, as this difference appears only in CD, we also considered the same conclusion as previously described above for WMs: the player’s profile potentially explains this outcome. It is assumed that maximum possible acceleration depends on initial velocity [48] but individual acceleration profile should also be considered to accurately quantify the real magnitude of the acceleration depending on the maximum that the player can achieve [47,49,50].
The discrepancy between descriptive values and the TMr highlights the importance of individualizing load monitoring. Relying solely on absolute thresholds may overestimate the demands for some players while underestimating them for others, particularly when using fixed speed zones for “high-speed running” [44]. The present findings within our team suggest that non-starters in specific roles may be subjected to “over-compensation” in mechanical variables (ACC/DCC) during mid-week, which could theoretically induce levels of fatigue that compromise their availability for selection [42].

7.1. Limitations

This study is limited by the relatively small sample size per playing position (n = 3–5), which may have limited the power to detect smaller interaction effects. Additionally, we used “maximal match demand” as the denominator for the TMr, which does not account for seasonal fluctuations in individual fitness. Furthermore, it is important to acknowledge that data recorded during competition are inherently influenced by the team’s playing style and tactical context. These factors must be considered when interpreting match demands and their comparison with training data, as they directly impact the calculation and subsequent interpretation of the training-to-match ratios. Moreover, it is essential to acknowledge the role of mental fatigue, as recent evidence suggests that cognitive fatigue can impair reaction time and fine-motor output without necessarily affecting broader physical performance measures. Future research should integrate measures of internal load (e.g., s-RPE), objective readiness (e.g., CMJ or HR-derived indices) and mental fatigue monitoring to determine if these higher compensatory TMr values in non-starters translate to superior performance or increased fatigue.

7.2. Practical Application

Based on the findings of this study, practitioners should consider the following recommendations to optimize microcycle load management: Firstly, implementing regular “top-up conditioning” sessions appears to reduce the gap in accumulated microcycle TMr between starters and non-starters, although the specific stimulus derived from match play load remains difficult to fully replicate on MD + 1/MD + 2. Secondly, the study revealed position-specific differences in the TMr during mid-week acquisition days. Special attention should be paid to the load prescribed to non-starting CD and WM players. Our data suggest these positions often reach higher relative intensities (ACC and HMLD) on acquisition days (MD − 4 and MD − 3) compared to other playing positions. Load prescription for these substitutes should be carefully calibrated to avoid excessive mechanical fatigue while ensuring that they reach the necessary “readiness” thresholds. However, the present data cannot determine whether the observed load patterns improved readiness or induced excessive fatigue, because no internal-load or readiness markers were collected, so this should be considered a possibility to be further analyzed. Interestingly, the disparity in certain TMr metrics suggests that starters might benefit from supplementary stimuli during the microcycle. To mitigate the “relative underloading” in specific variables compared to their non-starting peers, who undergo structured compensatory sessions, practitioners could implement targeted, low-volume “micro-doses” of high-intensity work for starters to ensure long-term maintenance of physical qualities without compromising recovery. This study highlights the need for individualized training load prescription to optimize match readiness across the entire squad. Finally, establishing individual and position-specific TMr profiles allows for more nuanced decision-making. Rather than using team-wide averages, using a player’s own maximal match demand as a benchmark enables a more precise identification of whether a player is over- or under-prepared for the competitive peak.

8. Conclusions

The longitudinal data presented in this study suggest that the training-to-match load ratio appears to be a useful tool for differentiating the preparation of starters and non-starters. By providing these position-specific benchmarks, this work offers a validated framework for practitioners to better understand and monitor compensatory strategies. While these strategies appear to attenuate the volume gap, practitioners should be mindful of position-specific “spikes” in mechanical load (ACC/DCC) during mid-week acquisition sessions, particularly for central defenders and wide midfielders. Ultimately, this dataset serves as a foundational reference for future studies to evaluate players’ readiness before acquisition days and the relationship with their training performance.

Author Contributions

Conceptualization, A.S.-C. and A.R.-F.; methodology, A.S.-C., F.M.C., D.C. and A.R.-F.; software, A.S.-C.; formal analysis, A.S.-C. and A.R.-F.; investigation, A.S.-C.; resources, A.S.-C., F.M.C., D.C. and A.R.-F.; data curation, A.S.-C.; writing—original draft preparation, A.S.-C., F.M.C., D.C. and A.R.-F.; writing—review and editing, A.S.-C., F.M.C., D.C. and A.R.-F.; visualization, A.S.-C., F.M.C., D.C. and A.R.-F.; supervision, A.R.-F.; project administration, A.R.-F. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study protocol was approved by the local ethics committee (ETICA-ULE-004-2021), and all procedures adhered to the Declaration of Helsinki.

Informed Consent Statement

Not applicable.

Data Availability Statement

The dataset is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0) https://doi.org/10.5281/zenodo.18986229; DOI 10.5281/zenodo.18986228.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
ACCHigh-intensity acceleration
CDCentral defender
CIConfidence interval
CMCentral midfielder
CMJCountermovement jump
DCCHigh-intensity deceleration
FBFullback
FWForward
GPSGlobal Positioning System
HMLDHigh metabolic load distance
HRHeart rate
HSDHigh-speed distance
LMMLinear mixed-effects models
MDMatch day
NSNon-starter
RPERating of perceived exertion
SStarters
SPDSprint distance
SSGsSmall-sided games
TDTotal distance
TMrTraining-to-match load ratio
WMWide midfielder

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Data 11 00084 g001
Figure 1. The microcycle accumulated training-to-match load ratios, normalized to individual maximal match demands in total distance (A), high-speed distance (B), sprint distance (C), high-intensity accelerations (D), high-intensity decelerations (E) and high metabolic load distance (F); NS = non-starter; S = starter; CD = central defender; FB = fullback; CM = central midfielder; WM = wide midfielder; FW = forward.
Figure 1. The microcycle accumulated training-to-match load ratios, normalized to individual maximal match demands in total distance (A), high-speed distance (B), sprint distance (C), high-intensity accelerations (D), high-intensity decelerations (E) and high metabolic load distance (F); NS = non-starter; S = starter; CD = central defender; FB = fullback; CM = central midfielder; WM = wide midfielder; FW = forward.
Data 11 00084 g001
Table 1. The total number of files across starting status and sessions.
Table 1. The total number of files across starting status and sessions.
Recovery/“Top-Up Conditioning” SessionsAcquisition PhaseTapering Phase
Training StatusMDMD + 1MD + 2MD − 5MD − 4MD − 3MD − 2MD − 1Total
NS18310191571022792012631277
S236296443822381802261098
Abbreviations: NS = non-starter; S = starter; MD = match day.
Table 2. Training load metrics (mean and ±standard deviation) across type of session, position and starting status.
Table 2. Training load metrics (mean and ±standard deviation) across type of session, position and starting status.
MDMD + 1MD + 2MD − 5MD − 4MD − 3MD − 2MD − 1
TDCDNS2143.4 ± 1251.2 *2334.8 ± 1301.1 *2621.3 ± 867.5 *4782.4 ± 575.65510.4 ± 655.75385.4 ± 757.15036.1 ± 1228.33179.7 ± 925.5
S9672.0 ± 1020.64964.9 ± 1727.44852.1 ± 787.74444.5 ± 850.95252.1 ± 1077.25373.3 ± 793.15172.9 ± 962.53173.3 ± 1057.6
FBNS3859.5 ± 2090.4 *4597.6 ± 737.5 *4959.7 ± 888.3 *4502.0 ± 774.65312.0 ± 1300.65776.6 ± 1146.55530.4 ± 960.03245.6 ± 978.8
S9925.7 ± 1124.62003.2 ± 756.32458.1 ± 734.74880.7 ± 1282.45556.0 ± 899.25681.4 ± 849.75060.2 ± 1065.23153.3 ± 1095.7
CMNS3709.1 ± 2047.4 *5186.2 ± 1739.0 *4194.8 ± 1008.7 *4585.1 ± 762.65573.1 ± 715.55655.8 ± 824.85159.9 ± 1175.42924.6 ± 945.4
S10,590.6 ± 1309.42106.9 ± 1205.92408.4 ± 785.64547.8 ± 724.75564.7 ± 737.85451.3 ± 970.85271.1 ± 1320.73039.8 ± 939.6
WMNS4138.7 ± 1877.8 *4797.3 ± 1435.6 *4265.8 ± 1125.3 *4589.9 ± 887.35608.2 ± 767.55527.3 ± 920.05114.1 ± 1147.22997.0 ± 923.0
S8991.3 ± 1590.22289.3 ± 1008.52229.6 ± 713.34415.5 ± 919.45438.8 ± 883.35567.7 ± 1041.05249.2 ± 1031.73018.9 ± 967.1
FWNS3930.6 ± 1141.6 *4018.4 ± 325.3 *4296.4 ± 947.2 *4571.0 ± 966.35254.6 ± 790.25115.0 ± 1190.14822.2 ± 1340.52814.5 ± 842.0
S8976.8 ± 1239.11794.0 ± 711.82557.9 ± 896.24678.4 ± 1136.85285.4 ± 854.95493.6 ± 681.14828.3 ± 1228.62995.0 ± 791.0
HSDCDNS170.3 ± 204.8 *202.6 ± 111.0 *266.7 ± 144.9 *81.9 ± 57.3154.2 ± 94.1148.5 ± 69.1116.9 ± 79.955.6 ± 79.2
S307.6 ± 124.184.4 ± 180.511.8 ± 17.589.4 ± 68.5133.6 ± 102.7152.6 ± 84.5137.9 ± 90.663.4 ± 68.9
FBNS259.8 ± 200.7 *227.4 ± 139.8 *325.9 ± 128.8 *100.8 ± 52.7331.7 ± 129.7315.9 ± 135.2317.9 ± 165.2102.4 ± 83.4
S599.5 ± 167.84.2 ± 8.56.0 ± 6.8164.9 ± 101.8258.7 ± 106.8321.6 ± 168.7264.4 ± 159.279.2 ± 62.2
CMNS230.3 ± 159.3 *170.4 ± 108.6 *218.9 ± 148.2 *72.9 ± 34.1149.5 ± 83.4153.4 ± 85.1127.7 ± 68.748.0 ± 70.1
S480.4 ± 134.532.6 ± 82.718.9 ± 45.5114.6 ± 52.4182.6 ± 92.3171.5 ± 100.1189.3 ± 131.356.3 ± 61.3
WMNS363.9 ± 192.6 *294.4 ± 155.0 *255.8 ± 166.9 *128.8 ± 85.4316.4 ± 131.1326.5 ± 124.4 *279.9 ± 144.873.4 ± 87.7
S695.5 ± 183.036.1 ± 89.21.0 ± 2.2106.5 ± 74.0235.9 ± 109.8254.2 ± 127.4245.9 ± 168.350.9 ± 38.3
FWNS253.3 ± 120.7 *255.6 ± 107.2 *255.3 ± 157.9 *118.0 ± 18.7194.9 ± 92.6213.2 ± 104.6207.0 ± 121.644.5 ± 48.8
S537.6 ± 139.01.5 ± 3.72.5 ± 4.4119.9 ± 82.0222.9 ± 131.3203.0 ± 79.8191.9 ± 115.634.1 ± 32.4
SPDCDNS49.5 ± 95.8 *60.0 ± 58.6118.4 ± 96.8 *12.5 ± 24.332.3 ± 49.834.5 ± 42.228.7 ± 54.328.7 ± 54.3
S117.7 ± 118.147.4 ± 99.42.0 ± 4.424.9 ± 48.154.2 ± 72.754.5 ± 63.449.7 ± 66.949.7 ± 66.9
FBNS157.2 ± 159.1 *157.2 ± 112.2 *224.8 ± 142.4 *50.2 ± 27.7227.3 ± 107.5192.7 ± 120.7184.6 ± 146.059.6 ± 66.3
S372.3 ± 167.80.0 ± 0.01.7 ± 4.083.1 ± 59.3145.3 ± 88.9178.1 ± 105.2137.5 ± 99.934.1 ± 36.7
CMNS78.1 ± 86.1 *63.1 ± 71.5 *122.8 ± 110.8 *12.1 ± 18.241.7 ± 40.038.2 ± 36.629.6 ± 27.816.9 ± 53.7
S172.3 ± 107.53.4 ± 9.12.4 ± 6.234.4 ± 38.458.8 ± 63.858.2 ± 69.562.0 ± 82.919.2 ± 39.6
WMNS179.0 ± 135.1 *157.4 ± 121.3 *159.1 ± 133.8 *45.9 ± 63.6144.9 ± 101.3158.7 ± 105.9 *133.1 ± 100.931.4 ± 63.7
S327.8 ± 177.67.1 ± 14.00.0 ± 0.044.1 ± 58.199.7 ± 82.4100.3 ± 88.996.7 ± 102.613.2 ± 14.6
FWNS132.1 ± 86.0 *160.4 ± 82.3 *172.8 ± 146.4 *30.6 ± 16.771.9 ± 69.190.4 ± 70.898.4 ± 80.311.5 ± 35.5
S260.1 ± 128.50.0 ± 0.00.0 ± 0.039.0 ± 46.093.9 ± 79.892.0 ± 68.579.4 ± 63.68.2 ± 10.4
ACCCDNS23.0 ± 16.0 *62.0 ± 24.2 *59.8 ± 15.9 *51.5 ± 13.752.0 ± 10.757.8 ± 14.948.1 ± 13.537.1 ± 11.8
S54.9 ± 10.316.8 ± 24.415.1 ± 10.149.1 ± 10.739.8 ± 11.049.0 ± 15.647.4 ± 11.534.0 ± 14.2
FBNS34.6 ± 16.0 *51.3 ± 15.9 *50.3 ± 22.1 *50.3 ± 22.147.9 ± 15.157.9 ± 19.252.0 ± 13.333.6 ± 11.8
S70.9 ± 16.310.8 ± 9.915.0 ± 10.015.0 ± 10.056.7 ± 15.264.1 ± 21.054.5 ± 9.937.3 ± 15.3
CMNS38.9 ± 14.6 *59.9 ± 15.9 *54.8 ± 17.9 *55.4 ± 13.452.1 ± 13.959.8 ± 18.050.0 ± 13.832.5 ± 13.7
S73.8 ± 14.416.7 ± 35.813.1 ± 10.358.3 ± 11.651.7 ± 12.156.1 ± 18.053.9 ± 15.632.9 ± 11.7
WMNS44.5 ± 17.4 *67.5 ± 19.4 *62.4 ± 20.8 *70.2 ± 16.467.1 ± 18.670.7 ± 18.963.8 ± 20.1 *33.4 ± 13.2
S75.9 ± 16.115.7 ± 21.513.5 ± 13.759.2 ± 16.454.1 ± 13.261.2 ± 18.353.5 ± 18.527.0 ± 10.2
FWNS41.6 ± 12.5 *52.5 ± 14.9 *55.8 ± 19.4 *57.6 ± 6.957.4 ± 16.258.1 ± 21.952.8 ± 23.133.2 ± 13.9
S80.6 ± 15.416.8 ± 30.79.2 ± 6.562.9 ± 19.653.5 ± 10.962.9 ± 20.048.8 ± 15.636.0 ± 13.9
DCCCDNS20.4 ± 13.4 *55.4 ± 19.4 *52.6 ± 15.2 *46.0 ± 13.149.0 ± 11.751.6 ± 15.844.5 ± 11.828.1 ± 11.4
S57.2 ± 11.110.6 ± 17.19.2 ± 8.543.9 ± 8.737.4 ± 13.545.8 ± 15.539.2 ± 13.326.3 ± 14.0
FBNS38.6 ± 17.8 *54.2 ± 10.9 *46.3 ± 19.9 *48.3 ± 21.053.9 ± 21.560.5 ± 18.455.2 ± 13.526.2 ± 10.4
S81.0 ± 12.66.2 ± 10.68.7 ± 7.256.7 ± 15.953.9 ± 19.059.0 ± 16.449.9 ± 12.528.7 ± 12.8
CMNS34.6 ± 14.7 *53.6 ± 14.2 *44.5 ± 14.4 *48.6 ± 15.349.8 ± 13.753.8 ± 16.244.1 ± 13.524.1 ± 10.4
S82.4 ± 12.713.0 ± 27.64.7 ± 3.449.8 ± 14.651.7 ± 11.854.7 ± 18.847.9 ± 17.825.7 ± 10.8
WMNS44.5 ± 20.2 *55.6 ± 21.7 *46.4 ± 20.6 *56.5 ± 18.260.4 ± 23.363.3 ± 20.153.8 ± 20.123.2 ± 11.9
S87.0 ± 33.38.6 ± 15.95.2 ± 4.949.8 ± 25.548.9 ± 24.054.7 ± 22.447.7 ± 22.120.5 ± 11.9
FWNS37.1 ± 15.4 *43.9 ± 11.4 *44.1 ± 15.2 *54.2 ± 10.152.6 ± 9.354.4 ± 21.050.4 ± 20.425.5 ± 11.3
S98.3 ± 20.39.5 ± 19.92.6 ± 3.564.1 ± 15.457.1 ± 15.763.2 ± 17.948.7 ± 14.132.0 ± 12.9
HMLDCDNS389.5 ± 284.7 *813.4 ± 290.0 *852.2 ± 199.9 *637.2 ± 172.5800.0 ± 173.5795.0 ± 136.0712.5 ± 232.4404.7 ± 159.6
S1333.5 ± 256.1227.4 ± 352.0152.8 ± 110.6698.2 ± 196.8674.8 ± 269.4743.6 ± 212.2706.7 ± 218.3397.9 ± 198.3
FBNS717.6 ± 389.0 *766.6 ± 226.8 *900.4 ± 299.0 *649.3 ± 169.3953.3 ± 240.41019.9 ± 250.3901.8 ± 350.0422.3 ± 172.7
S1755.4 ± 254.395.0 ± 90.8151.6 ± 86.3783.0 ± 280.3891.1 ± 238.6949.7 ± 329.3828.8 ± 346.7422.6 ± 196.8
CMNS689.4 ± 393.9 *799.6 ± 283.1 *660.2 ± 215.1 *701.0 ± 164.4893.1 ± 198.9893.5 ± 188.8789.5 ± 230.0341.6 ± 138.7
S1951.1 ± 380.8146.9 ± 306.0139.0 ± 85.7725.4 ± 198.2908.8 ± 198.3892.0 ± 247.2879.3 ± 334.1366.3 ± 151.5
WMNS892.4 ± 410.7 *870.0 ± 346.4 *727.0 ± 302.2 *727.2 ± 168.9996.6 ± 239.61028.5 ± 242.9 *909.8 ± 295.5355.8 ± 164.0
S1873.1 ± 406.0177.1 ± 299.089.8 ± 66.2640.0 ± 227.3890.7 ± 228.8912.8 ± 263.8847.6 ± 293.2319.0 ± 121.3
FWNS726.0 ± 239.4 *686.8 ± 135.3 *673.8 ± 216.1 *730.8 ± 86.1864.7 ± 199.8843.4 ± 255.6799.6 ± 322.8339.3 ± 132.2
S1722.7 ± 271.290.3 ± 146.186.6 ± 46.9719.4 ± 239.2835.6 ± 236.7884.6 ± 167.6746.0 ± 247.0369.1 ± 137.3
Abbreviations: NS = non-starter; S = starter; TD = total distance; HSD = high-speed distance; SPD = sprint distance; ACC = high-intensity acceleration; DCC = high-intensity deceleration; HMLD = high metabolic load distance; CD = central defender; FB = fullback; CM = central midfielder; WM = wide midfielder; FW = forward. * Significant differences with starter players (p < 0.05).
Table 3. Daily external load ratios across (mean and ±standard deviation) across type of session. position and starting status.
Table 3. Daily external load ratios across (mean and ±standard deviation) across type of session. position and starting status.
MDMD + 1MD + 2MD − 5MD − 4MD − 3MD − 2MD − 1
TDCDNS0.2 ± 0.1 *0.5 ± 0.2 *0.5 ± 0.1 *0.5 ± 0.10.6 ± 0.10.6 ± 0.10.5 ± 0.10.3 ± 0.1
S1.0 ± 0.10.2 ± 0.10.3 ± 0.10.5 ± 0.10.5 ± 0.10.6 ± 0.10.5 ± 0.10.3 ± 0.1
FBNS0.4 ± 0.2 *0.5 ± 0.1 *0.5 ± 0.1 *0.5 ± 0.10.5 ± 0.10.6 ± 0.10.6 ± 0.10.3 ± 0.1
S1.0 ± 0.10.2 ± 0.10.2 ± 0.10.5 ± 0.10.6 ± 0.10.6 ± 0.10.5 ± 0.10.3 ± 0.1
CMNS0.4 ± 0.2 *0.5 ± 0.2 *0.4 ± 0.1 *0.4 ± 0.10.5 ± 0.10.6 ± 0.10.5 ± 0.10.3 ± 0.1
S1.0 ± 0.10.2 ± 0.10.2 ± 0.10.5 ± 0.10.5 ± 0.10.5 ± 0.10.5 ± 0.10.3 ± 0.1
WMNS0.5 ± 0.2 *0.6 ± 0.2 *0.5 ± 0.1 *0.6 ± 0.10.7 ± 0.10.7 ± 0.10.6 ± 0.20.4 ± 0.1
S1.0 ± 0.10.2 ± 0.10.3 ± 0.10.5 ± 0.10.6 ± 0.10.6 ± 0.10.6 ± 0.10.3 ± 0.1
FWNS0.4 ± 0.1 *0.5 ± 0.1 *0.5 ± 0.1 *0.5 ± 0.10.6 ± 0.10.6 ± 0.10.5 ± 0.10.3 ± 0.1
S1.0 ± 0.10.2 ± 0.10.3 ± 0.10.5 ± 0.10.6 ± 0.10.6 ± 0.10.5 ± 0.10.3 ± 0.1
HSDCDNS0.6 ± 0.7 *0.6 ± 0.3 *0.9 ± 0.5 *0.3 ± 0.20.5 ± 0.30.5 ± 0.30.4 ± 0.30.2 ± 0.2
S1.0 ± 0.30.3 ± 0.70.0 ± 0.10.3 ± 0.20.4 ± 0.30.5 ± 0.30.4 ± 0.30.2 ± 0.2
FBNS0.4 ± 0.3 *0.4 ± 0.2 *0.5 ± 0.2 *0.2 ± 0.10.5 ± 0.20.5 ± 0.20.5 ± 0.20.2 ± 0.1
S1.0 ± 0.30.0 ± 0.00.0 ± 0.00.3 ± 0.20.4 ± 0.20.5 ± 0.30.5 ± 0.30.1 ± 0.1
CMNS0.8 ± 0.7 *0.7 ± 0.5 *0.9 ± 0.7 *0.2 ± 0.10.5 ± 0.30.5 ± 0.30.5 ± 0.20.2 ± 0.3
S1.0 ± 0.20.1 ± 0.20.0 ± 0.10.2 ± 0.10.4 ± 0.20.4 ± 0.20.4 ± 0.30.1 ± 0.1
WMNS0.5 ± 0.3 *0.4 ± 0.2 *0.3 ± 0.2 *0.2 ± 0.10.5 ± 0.20.5 ± 0.20.4 ± 0.20.1 ± 0.1
S1.0 ± 0.20.1 ± 0.10.0 ± 0.00.2 ± 0.10.3 ± 0.20.4 ± 0.20.3 ± 0.20.1 ± 0.1
FWNS0.5 ± 0.2 *0.5 ± 0.2 *0.5 ± 0.3 *0.2 ± 0.00.3 ± 0.20.4 ± 0.20.4 ± 0.20.1 ± 0.1
S1.0 ± 0.20.0 ± 0.00.0 ± 0.00.2 ± 0.10.4 ± 0.20.4 ± 0.10.3 ± 0.20.1 ± 0.1
SPDCDNS0.7 ± 1.20.6 ± 0.4 *0.8 ± 0.8 *0.1 ± 0.20.5 ± 0.60.5 ± 0.60.3 ± 0.50.1 ± 0.3
S1.0 ± 0.60.3 ± 0.60.0 ± 0.00.1 ± 0.20.3 ± 0.30.5 ± 0.40.4 ± 0.50.1 ± 0.3
FBNS0.4 ± 0.4 *0.4 ± 0.3 *0.7 ± 0.4 *0.1 ± 0.10.5 ± 0.30.5 ± 0.20.5 ± 0.30.1 ± 0.2
S1.0 ± 0.40.0 ± 0.00.0 ± 0.00.2 ± 0.10.4 ± 0.30.5 ± 0.30.4 ± 0.30.1 ± 0.1
CMNS0.8 ± 0.7 *0.9 ± 1.0 *1.8 ± 1.6 *0.1 ± 0.20.5 ± 0.50.5 ± 0.50.4 ± 0.40.2 ± 0.8
S1.0 ± 0.50.0 ± 0.10.0 ± 0.00.2 ± 0.20.3 ± 0.20.3 ± 0.30.3 ± 0.40.1 ± 0.3
WMNS0.5 ± 0.3 *0.4 ± 0.3 *0.4 ± 0.4 *0.1 ± 0.20.4 ± 0.20.4 ± 0.20.3 ± 0.20.1 ± 0.2
S1.0 ± 0.30.0 ± 0.10.0 ± 0.00.1 ± 0.10.3 ± 0.20.3 ± 0.20.3 ± 0.30.0 ± 0.1
FWNS0.5 ± 0.3 *0.5 ± 0.2 *0.7 ± 0.6 *0.1 ± 0.00.2 ± 0.20.3 ± 0.20.4 ± 0.30.0 ± 0.1
S1.0 ± 0.40.0 ± 0.00.0 ± 0.00.1 ± 0.10.4 ± 0.30.3 ± 0.30.3 ± 0.20.0 ± 0.0
ACCCDNS0.4 ± 0.3 *1.0 ± 0.4 *1.0 ± 0.3 *0.9 ± 0.20.9 ± 0.2 *1.0 ± 0.20.8 ± 0.20.6 ± 0.2
S1.0 ± 0.20.3 ± 0.40.3 ± 0.20.9 ± 0.20.7 ± 0.20.9 ± 0.30.9 ± 0.20.6 ± 0.3
FBNS0.6 ± 0.3 *0.9 ± 0.3 *0.8 ± 0.3 *0.9 ± 0.20.8 ± 0.31.0 ± 0.30.9 ± 0.20.6 ± 0.2
S1.0 ± 0.20.1 ± 0.10.2 ± 0.21.0 ± 0.30.8 ± 0.20.9 ± 0.30.8 ± 0.20.5 ± 0.2
CMNS0.5 ± 0.2 *0.8 ± 0.2 *0.7 ± 0.2 *0.7 ± 0.20.7 ± 0.20.8 ± 0.30.7 ± 0.20.4 ± 0.2
S1.0 ± 0.10.3 ± 0.60.2 ± 0.10.9 ± 0.20.7 ± 0.20.8 ± 0.30.8 ± 0.20.5 ± 0.2
WMNS0.6 ± 0.2 *0.8 ± 0.2 *0.8 ± 0.2 *0.9 ± 0.30.8 ± 0.20.9 ± 0.30.8 ± 0.30.4 ± 0.2
S1.0 ± 0.20.2 ± 0.20.2 ± 0.20.8 ± 0.20.7 ± 0.20.8 ± 0.20.7 ± 0.30.3 ± 0.1
FWNS0.5 ± 0.2 *0.7 ± 0.1 *0.7 ± 0.2 *0.7 ± 0.20.7 ± 0.20.7 ± 0.30.7 ± 0.30.4 ± 0.2
S1.0 ± 0.20.2 ± 0.30.1 ± 0.10.8 ± 0.20.7 ± 0.10.8 ± 0.20.6 ± 0.20.5 ± 0.2
DCCCDNS0.3 ± 0.2 *0.8 ± 0.3 *0.8 ± 0.3 *0.7 ± 0.20.8 ± 0.2 *0.8 ± 0.20.7 ± 0.20.4 ± 0.2
S1.0 ± 0.20.2 ± 0.30.2 ± 0.10.8 ± 0.20.7 ± 0.20.8 ± 0.30.7 ± 0.20.5 ± 0.2
FBNS0.5 ± 0.2 *0.7 ± 0.1 *0.6 ± 0.2 *0.6 ± 0.30.7 ± 0.30.8 ± 0.20.7 ± 0.20.3 ± 0.1
S1.0 ± 0.10.1 ± 0.10.1 ± 0.10.7 ± 0.20.7 ± 0.20.7 ± 0.20.6 ± 0.10.4 ± 0.1
CMNS0.4 ± 0.2 *0.7 ± 0.2 *0.6 ± 0.2 *0.6 ± 0.20.6 ± 0.20.7 ± 0.20.6 ± 0.20.3 ± 0.1
S1.0 ± 0.10.2 ± 0.40.1 ± 0.00.6 ± 0.20.7 ± 0.20.7 ± 0.20.6 ± 0.20.3 ± 0.1
WMNS0.5 ± 0.2 *0.7 ± 0.2 *0.5 ± 0.2 *0.7 ± 0.20.7 ± 0.20.8 ± 0.2 *0.6 ± 0.20.3 ± 0.1
S1.0 ± 0.20.1 ± 0.10.1 ± 0.10.6 ± 0.10.6 ± 0.20.6 ± 0.20.6 ± 0.20.2 ± 0.1
FWNS0.4 ± 0.1 *0.5 ± 0.1 *0.5 ± 0.2 *0.7 ± 0.10.6 ± 0.10.6 ± 0.20.6 ± 0.20.3 ± 0.1
S1.0 ± 0.10.1 ± 0.20.0 ± 0.00.6 ± 0.10.6 ± 0.10.7 ± 0.20.5 ± 0.20.3 ± 0.1
HMLDCDNS0.3 ± 0.2 *0.6 ± 0.2 *0.6 ± 0.1 *0.5 ± 0.10.6 ± 0.1 *0.6 ± 0.10.5 ± 0.20.3 ± 0.1
S1.0 ± 0.20.2 ± 0.30.1 ± 0.10.5 ± 0.10.5 ± 0.20.6 ± 0.10.5 ± 0.10.3 ± 0.1
FBNS0.4 ± 0.2 *0.4 ± 0.1 *0.5 ± 0.2 *0.4 ± 0.10.5 ± 0.10.6 ± 0.10.5 ± 0.20.2 ± 0.1
S1.0 ± 0.10.1 ± 0.10.1 ± 0.10.5 ± 0.10.5 ± 0.10.5 ± 0.20.5 ± 0.20.2 ± 0.1
CMNS0.4 ± 0.2 *0.5 ± 0.2 *0.4 ± 0.1 *0.4 ± 0.10.5 ± 0.10.5 ± 0.1 *0.5 ± 0.10.2 ± 0.1
S1.0 ± 0.10.1 ± 0.20.1 ± 0.00.4 ± 0.10.5 ± 0.20.5 ± 0.10.5 ± 0.20.2 ± 0.1
WMNS0.5 ± 0.2 *0.5 ± 0.2 *0.4 ± 0.2 *0.4 ± 0.10.6 ± 0.10.6 ± 0.2 *0.5 ± 0.20.2 ± 0.1
S1.0 ± 0.20.1 ± 0.10.1 ± 0.00.3 ± 0.10.5 ± 0.20.5 ± 0.20.5 ± 0.20.2 ± 0.1
FWNS0.4 ± 0.1 *0.4 ± 0.1 *0.4 ± 0.1 *0.4 ± 0.10.5 ± 0.10.5 ± 0.10.5 ± 0.20.2 ± 0.1
S1.0 ± 0.10.1 ± 0.10.1 ± 0.00.4 ± 0.10.5 ± 0.10.5 ± 0.10.4 ± 0.10.2 ± 0.1
Abbreviations: NS = non-starter; S = starter; TD = total distance; HSD = high-speed distance; SPD = sprint distance; ACC = high-intensity acceleration; DCC = high-intensity deceleration; HMLD = high metabolic load distance; CD = central defender; FB = fullback; CM = central midfielder; WM = wide midfielder; FW = forward. * Significant differences with starter players (p < 0.05).
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Sierra-Casas, A.; Castillo, D.; Clemente, F.M.; Rodríguez-Fernández, A. Daily and Accumulated Training-to-Match Load Ratios in Professional Soccer: The Influence of Starting Status and Playing Position Across a Full Competitive Season. Data 2026, 11, 84. https://doi.org/10.3390/data11040084

AMA Style

Sierra-Casas A, Castillo D, Clemente FM, Rodríguez-Fernández A. Daily and Accumulated Training-to-Match Load Ratios in Professional Soccer: The Influence of Starting Status and Playing Position Across a Full Competitive Season. Data. 2026; 11(4):84. https://doi.org/10.3390/data11040084

Chicago/Turabian Style

Sierra-Casas, Alejandro, Daniel Castillo, Filipe Manuel Clemente, and Alejandro Rodríguez-Fernández. 2026. "Daily and Accumulated Training-to-Match Load Ratios in Professional Soccer: The Influence of Starting Status and Playing Position Across a Full Competitive Season" Data 11, no. 4: 84. https://doi.org/10.3390/data11040084

APA Style

Sierra-Casas, A., Castillo, D., Clemente, F. M., & Rodríguez-Fernández, A. (2026). Daily and Accumulated Training-to-Match Load Ratios in Professional Soccer: The Influence of Starting Status and Playing Position Across a Full Competitive Season. Data, 11(4), 84. https://doi.org/10.3390/data11040084

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